Urban Scale Cooling Load Prediction of High-Rise Buildings in a Hot and Arid Climate
Bibliographic record
Abstract
Abstract This study employs an archetype-based modeling approach to estimate and analyze the urban scale cooling load profile of high-rise buildings in the Marina district of Lusail City, Qatar. Since the Marina district is a newly built district, the building typology and geometric characteristics are considered the main criteria for the selection of representative archetypes of the district. Three high-rise building archetypes are developed using EnergyPlus software to represent the available building stocks in the district, which are residential, commercial, and mixed-use. Required data for the input parameters are collected from various sources such as the Lusail City GSAS 2 Star Rating Guidelines, which define the minimum requirements in the region, ASHRAE 90.1, and ASHRAE 62.1 standards, along with user surveys when available. Detailed cooling load profiles of the three building archetypes are obtained in EnergyPlus, which enables aggregating the cooling loads to obtain the cooling load profile of the Marina district at various time resolutions. The cooling load profiles obtained after the simulation of each building archetype model in EnergyPlus are validated with real building cooling loads measured in the case study area. The developed cooling load profiles in this study can inform district cooling facilities for an optimal design and operation of the plant, reveal the energy-saving potential of buildings, and aid in defining cost allocations or billing strategies for end users without the need for the installation of zone-level submeter to each apartment unit. This study further contributes to the establishment of a representative building archetype library for hot and arid climate zones. Thus, the building archetype models produced for the Marina district in this study are also applicable to other regions with similar building types and climatic characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".